Furthermore, most dense subgraph discovery algorithms are designed to capture only the first-order relationships.
Higher-order structures (i.e., motifs/graphlets) are shown to be the fundamental building blocks in the organization and dynamics of real-world networks such as social and neural networks [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
Discovering dense subgraphs that have a higher-order structure is imperative in the analysis of those networks.
In simple networks, motifs are used to find subgraphs with higher-order structure, which cannot be detected with edge-centric methods [7]}, [8]}.
However, it is not clear how to find dense subgraphs with motifs in heterogeneous networks.
The spectrum of motifs in heterogeneous networks is wide due to the edge directions and node/edge attributes.
Although this variety is particularly effective for the analysis as the structure and dynamics can vary with respect to the type of motif considered [9]}, [10]}, [11]}, handling the diverse nature of heterogeneous networks while watching for the motifs is a challenging problem.
